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        <title>Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data</title>
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    <abstract>Large language models are being rapidly applied across many fields such as healthcare, finance, transportation, and energy, among many others. These applications often involve analyzing time-series data alongside contextual information in the form of natural language to support informed decisions. However, current time-series models are limited in their ability to perform reasoning that involves both time-series and their textual content. In this work, we address this gap by introducing Chat-TS, a large language model (LLM) based framework designed to support reasoning over time series and textual data. Unlike traditional models, Chat-TS integrates time-series tokens into LLMs’ vocabulary, enhancing its reasoning ability over both modalities without compromising core natural language capabilities. To support learning and evaluation, we contribute new datasets: the TS Instruct Training Dataset (pairing diverse time-series data with relevant text instructions and responses for instruction tuning), the TS Instruct Question and Answer (QA) Gold Dataset (multiple-choice questions to evaluate multimodal reasoning), and a TS Instruct Quantitative Probing Set (a small subset of TS Instruct QA reasoning tasks alongside math and decision-making questions for LLM evaluation). We design a training strategy to preserve the inherent reasoning capabilities of LLMs while augmenting them for time-series reasoning. Experiments show that Chat-TS achieves state-of-the-art performance in multimodal reasoning tasks by maintaining strong natural language proficiency while improving time-series reasoning.</abstract>
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%0 Conference Proceedings
%T Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data
%A Quinlan, Paul
%A Li, Qingguo
%A Zhu, Xiaodan
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F quinlan-etal-2026-chat
%X Large language models are being rapidly applied across many fields such as healthcare, finance, transportation, and energy, among many others. These applications often involve analyzing time-series data alongside contextual information in the form of natural language to support informed decisions. However, current time-series models are limited in their ability to perform reasoning that involves both time-series and their textual content. In this work, we address this gap by introducing Chat-TS, a large language model (LLM) based framework designed to support reasoning over time series and textual data. Unlike traditional models, Chat-TS integrates time-series tokens into LLMs’ vocabulary, enhancing its reasoning ability over both modalities without compromising core natural language capabilities. To support learning and evaluation, we contribute new datasets: the TS Instruct Training Dataset (pairing diverse time-series data with relevant text instructions and responses for instruction tuning), the TS Instruct Question and Answer (QA) Gold Dataset (multiple-choice questions to evaluate multimodal reasoning), and a TS Instruct Quantitative Probing Set (a small subset of TS Instruct QA reasoning tasks alongside math and decision-making questions for LLM evaluation). We design a training strategy to preserve the inherent reasoning capabilities of LLMs while augmenting them for time-series reasoning. Experiments show that Chat-TS achieves state-of-the-art performance in multimodal reasoning tasks by maintaining strong natural language proficiency while improving time-series reasoning.
%U https://aclanthology.org/2026.eacl-long.263/
%P 5621-5647

Markdown (Informal)

[Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data](https://aclanthology.org/2026.eacl-long.263/) (Quinlan et al., EACL 2026)

• Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data (Quinlan et al., EACL 2026)

ACL

• Paul Quinlan, Qingguo Li, and Xiaodan Zhu. 2026. Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5621–5647, Rabat, Morocco. Association for Computational Linguistics.